susieR

An R package for "sum of single effects" (SuSiE) regression.

SuSiE's single effect model is based on simple univariate Bayesian regression applied in the context of multiple regression with the assumption that there is exactly one variable has a non-zero effect. It extends the single effect assumption to multiple regression with more than one non-zero effect using a simple and intuitive model fitting procedure based on variational inference. It provides approximate posterior distributions from which quantities such as posterior inclusion probability, posterior mean effect size and 95% confidence sets of select variables can be derived. Particularly, the SuSiE model structure and algorithm results in readily interpretable output in the context of genetic fine-mapping studies where variables are highly correlated due to Linkage Disequilibrium.

SuSiE model is developed by Matthew Stephens and his team at the University of Chicago.

This is very much work in progress. Please post issues to ask questions, get our support or provide us feedback; please send pull requests if you have helped fixing bugs or making improvements to the source code.

Setup

To automatically retrieve and install susieR from this repository,

devtools::install_github("stephenslab/susieR")

Quick Start

Here is a quick document to show susieR in action. For more documentation and examples please visit: https://stephenslab.github.io/susieR

Developer notes

• When any changes are made to roxygen2 markup, simply run devtools::document() to update package NAMESPACE and documentation files.

• Run pkgdown::build_site() to build the website. Getting pkgdown to work properly can be frustrating due to numerous & fragile dependencies. If pkgdown does not work for you out of the box you can use this docker command to run all vignettes and build the site:

docker run --rm --security-opt label:disable -t -P -w $PWD -v $PWD:$PWD \
	-u $UID:${GROUPS[0]} -e HOME=/home/$USER -e USER=$USER gaow/susie \
	R --slave -e "pkgdown::build_site(lazy=TRUE, examples=FALSE)"